For operational efficiency retailers rely on accurate demand forecasts for individual Stock Keeping Units (SKUs). Order decisions need to ensure that the inventory level is not too high, to avoid high inventory costs, and not too low to avoid stock out and lost sales. Better forecasts at the SKU-store level can lead to better order quantity decisions. Forecasting stock keeping unit (SKU) sales in the presence of promotions is a particularly challenging task. Various studies in the marketing literature have shown that promotions, temporary price reductions, even a simple display have significant impact on the sales of the promoted product, and other products in the category. The sales in the previous and subsequent time periods are also affected as shoppers adjust purchasing behavior in anticipation of promotions or due to accumulation of purchased product. Further, seasonality, the preferences of the clientele at the particular store, the size of the package can all affect the size of the impact. Even more challenging is prediction of sales for new SKUs where sufficient data for constructing a model for the SKU does not exist. The SKU life cycle is becoming shorter, and the store manager is increasingly faced with the problem of providing an order quantity for a new SKU before its first promotion.
Several studies stress the need for model simplicity and ease of communication for user acceptance. Retail managers want to get a sense for how the predictions are made before they allow deployment of decision support models that have been trained (estimated) based on historical data. Beyond a face validity check, they want to make sure that the phenomena that the models have captured will continue to be present in the future time periods. Further, insights into store, brand and category dynamics are very valuable for the retail managers, who take pricing, promotion, and assortment decisions.